{"id":"W4254896949","doi":"10.1520/jte20170635","title":"Train Internal Noise Due to Wheel-Rail Interaction","year":2018,"lang":"en","type":"article","venue":"Journal of Testing and Evaluation","topic":"Railway Engineering and Dynamics","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Statistical energy analysis; Train; Vibration; Noise (video); Acoustics; Finite element method; Computation; Coupling (piping); Structural engineering; Automotive engineering; Energy (signal processing); Engineering; Computer science; Physics; Mechanical engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003000128,0.0003244714,0.0003005608,0.0003080525,0.0002117272,0.0003335175,0.0002356603,0.0003343446,0.001737829],"category_scores_gemma":[0.001150264,0.0001480834,0.0002616317,0.0001648849,0.0005395797,0.0002959227,0.0005058103,0.0001769021,0.0002248224],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001756936,"about_ca_system_score_gemma":0.0001291857,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001521099,"about_ca_topic_score_gemma":0.001265009,"domain_scores_codex":[0.9996524,0.00008453982,0.00001238294,0.00004164898,0.0001723206,0.00003665617],"domain_scores_gemma":[0.9994425,0.0003314887,0.00005931198,0.00003940813,0.0001042031,0.00002303246],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001318269,0.0001410127,0.02483513,0.000361052,0.0001229536,0.001265584,0.0008649123,0.3746069,0.5252783,0.002287338,0.0005428006,0.06837565],"study_design_scores_gemma":[0.00003564149,0.0005217951,0.05935986,0.00004991695,0.0001826548,0.0005198753,0.0004112335,0.7781309,0.1583652,0.001026543,0.001336977,0.00005948126],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8710594,0.0001066609,0.1239826,0.00003961904,0.00002921837,0.00001630746,0.0000377533,0.0003202085,0.00440836],"genre_scores_gemma":[0.9981769,0.00002113365,0.001004608,0.000007252972,0.000003961643,0.000004347803,0.00001622974,0.00001945664,0.0007460807],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001737829,"threshold_uncertainty_score":0.005813658,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03312456263061201,"score_gpt":0.2871526006662246,"score_spread":0.2540280380356126,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}